U.S. patent number 10,896,378 [Application Number 15/859,963] was granted by the patent office on 2021-01-19 for fast detection of energy consumption anomalies in buildings.
This patent grant is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Amadou Ba, Joern Ploennigs.
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United States Patent |
10,896,378 |
Ba , et al. |
January 19, 2021 |
Fast detection of energy consumption anomalies in buildings
Abstract
Embodiments for detection of energy consumption anomalies in one
or more energy consumption systems in a cloud computing environment
by a processor. Energy consumption may be predicted for one or more
facilities according to one or more energy consumption
measurements, weather data, and one or more characteristics of the
one or more facilities, or a combination thereof. An onset of an
energy consumption anomaly may be detected according to the
prediction.
Inventors: |
Ba; Amadou (Dublin,
IE), Ploennigs; Joern (Dublin, IE) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION (Armonk, NY)
|
Appl.
No.: |
15/859,963 |
Filed: |
January 2, 2018 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20190205774 A1 |
Jul 4, 2019 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05B
15/02 (20130101); G06N 5/04 (20130101) |
Current International
Class: |
G06N
5/04 (20060101); G05B 15/02 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Shechtman; Sean
Attorney, Agent or Firm: Griffiths & Seaton PLLC
Claims
The invention claimed is:
1. A method for detection of energy consumption in a computing
environment by a processor, comprising: monitoring, by the
processor, data collected by one or more Internet of Things (IoT)
sensor devices, wherein the data is representative of a combination
of one or more energy consumption measurements, weather data, and
one or more characteristics of one or more facilities; training,
using a machine learning operation by the processor, one or more
prediction models according to the monitored data, wherein the one
or more prediction models compute, in real-time, residuals for the
one or more facilities as a difference between a predicted energy
consumption and an actual energy consumption of the one or more
facilities; predicting, by the processor, energy consumption for
the one or more facilities according to an analyzation of the
monitored data by the trained one or more prediction models,
wherein, when at least some of the residuals begin to deviate from
a standardized nominal value, one or more tuning parameters of the
one or more prediction models are dynamically changed; and
detecting, by the processor, an onset of an energy consumption
anomaly according to the prediction.
2. The method of claim 1, further including alerting the one or
more IoT devices of the energy consumption anomaly.
3. The method of claim 1, further including identifying a location
of the energy consumption anomaly according to the monitored data
collected by the one or more IoT sensor devices associated with the
one or more facilities, wherein the one or more IoT sensor devices
are in an IoT computing network.
4. The method of claim 1, further including monitoring the energy
consumption using a change point detection operation, a hypothesis
test, or a combination thereof.
5. The method of claim 1, further including cognitively learning or
estimating the one or more tuning parameters of one or more
prediction models for predicting the energy consumption.
6. The method of claim 1, further including comparing the predicted
energy consumption with an energy consumption threshold to detect
the energy consumption anomaly.
7. A system for detection of energy consumption in a computing
environment, comprising: one or more computers with executable
instructions that when executed cause the system to: monitor, by a
processor associated with the one or more computers and executing
the executable instructions, data collected by one or more Internet
of Things (IoT) sensor devices, wherein the data is representative
of a combination of one or more energy consumption measurements,
weather data, and one or more characteristics of one or more
facilities; train, using a machine learning operation by the
processor, one or more prediction models according to the monitored
data, wherein the one or more prediction models compute, in
real-time, residuals for the one or more facilities as a difference
between a predicted energy consumption and an actual energy
consumption of the one or more facilities; predict, by the
processor, energy consumption for the one or more facilities
according to an analyzation of the monitored data by the trained
one or more prediction models, wherein, when at least some of the
residuals begin to deviate from a standardized nominal value, one
or more tuning parameters of the one or more prediction models are
dynamically changed; and detect, by the processor, an onset of an
energy consumption anomaly according to the prediction.
8. The system of claim 7, wherein the executable instructions
further alert the one or more IoT devices of the energy consumption
anomaly.
9. The system of claim 7, wherein the executable instructions
further identify a location of the energy consumption anomaly
according to the monitored data collected by the one or more IoT
sensor devices associated with the one or more facilities, wherein
the one or more IoT sensor devices are in an IoT computing
network.
10. The system of claim 7, wherein the executable instructions
further monitor the energy consumption using a change point
detection operation, a hypothesis test, or a combination
thereof.
11. The system of claim 7, wherein the executable instructions
further cognitively learn or estimate the one or more tuning
parameters of one or more prediction models for predicting the
energy consumption.
12. The system of claim 7, wherein the executable instructions
further compare the predicted energy consumption with an energy
consumption threshold to detect the energy consumption anomaly.
13. A computer program product for detection of energy consumption
in a building associated with a computing environment by a
processor, the computer program product comprising a non-transitory
computer-readable storage medium having computer-readable program
code portions stored therein, the computer-readable program code
portions comprising: an executable portion that monitors, by the
processor, data collected by one or more Internet of Things (IoT)
sensor devices, wherein the data is representative of a combination
of one or more energy consumption measurements, weather data, and
one or more characteristics of one or more facilities; an
executable portion that trains, using a machine learning operation
by the processor, one or more prediction models according to the
monitored data, wherein the one or more prediction models compute,
in real-time, residuals for the one or more facilities as a
difference between a predicted energy consumption and an actual
energy consumption of the one or more facilities; an executable
portion that predicts, by the processor, energy consumption for one
or more facilities according to an analyzation of the monitored
data by the trained one or more prediction models, wherein, when at
least some of the residuals begin to deviate from a standardized
nominal value, one or more tuning parameters of the one or more
prediction models are dynamically changed; and an executable
portion that detects, by the processor, an onset of an energy
consumption anomaly according to the prediction.
14. The computer program product of claim 13, further including an
executable portion that alerts the one or more IoT devices of the
energy consumption anomaly.
15. The computer program product of claim 13, further including an
executable portion that identifies a location of the energy
consumption anomaly according to the monitored data collected by
the one or more IoT sensor devices associated with the one or more
facilities, wherein the one or more IoT sensor devices are in an
IoT computing network.
16. The computer program product of claim 13, further including an
executable portion that monitors the energy consumption using a
change point detection operation, a hypothesis test, or a
combination thereof.
17. The computer program product of claim 13, further including an
executable portion that cognitively learns or estimates the one or
more tuning parameters of one or more prediction models for
predicting the energy consumption.
18. The computer program product of claim 13, further including an
executable portion that compares the predicted energy consumption
with an energy consumption threshold to detect the energy
consumption anomaly.
Description
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates in general to computing systems, and
more particularly to, various embodiments for cognitive energy
consumption anomaly detection in a building associated with a cloud
computing environment using a computing processor.
Description of the Related Art
In today's society, various advances in mechanical systems, coupled
with advances in computing technology have made possible a wide
variety of attendant benefits, such as increasing the efficiency of
fluid transfer pumping systems. As computers proliferate throughout
aspects of society, additional opportunities continue to present
themselves for leveraging technology in energy management systems
for improving efficiency of power and energy consumption.
SUMMARY OF THE INVENTION
Various embodiments for cognitive detection of energy consumption
anomalies in one or more buildings (e.g., energy consumption
systems in a building) that may be associated with a cloud
computing environment by a processor are provided. In one
embodiment, by way of example only, a method/system for fast
detection of abnormal energy consumption of energy consumption
systems of a building using an array of Internet of Things (IoT)
sensors in a computing environment is provided. Energy consumption
may be predicted for one or more facilities according to one or
more energy consumption measurements, weather data, and one or more
characteristics of the one or more facilities, or a combination
thereof. An onset of an energy consumption anomaly may be detected
according to the prediction.
BRIEF DESCRIPTION OF THE DRAWINGS
In order that the advantages of the invention will be readily
understood, a more particular description of the invention briefly
described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict only typical embodiments
of the invention and are not therefore to be considered to be
limiting of its scope, the invention will be described and
explained with additional specificity and detail through the use of
the accompanying drawings, in which:
FIG. 1 is a block diagram depicting an exemplary computing node
according to an embodiment of the present invention;
FIG. 2 is an additional block diagram depicting an exemplary cloud
computing environment according to an embodiment of the present
invention;
FIG. 3 is an additional block diagram depicting abstraction model
layers according to an embodiment of the present invention;
FIG. 4 is a diagram depicting various user hardware and computing
components functioning in accordance with aspects of the present
invention;
FIG. 5 is a flowchart diagram of an exemplary method for cognitive
detection of energy consumption anomalies using one or more
Internet of Things (IoT) devices ("smart recorders") operating
within a computing network in accordance with aspects of the
present invention;
FIG. 6 is a flowchart diagram of an exemplary method for monitoring
energy consumption in one or more facilities operating within a
computing network in accordance with aspects of the present
invention;
FIG. 7 is a flowchart diagram of an exemplary method for
forecasting energy consumption in one or more facilities in an
Internet of Things (IoT) computing network in accordance with
aspects of the present invention; and
FIG. 8 is a flowchart diagram of an exemplary method for detection
of energy consumption anomalies of one or more facilities in an
Internet of Things (IoT) computing network in accordance with
aspects of the present invention.
DETAILED DESCRIPTION OF THE DRAWINGS
Energy consumption, monitoring, and management are crucial
components of infrastructures such as, for example, buildings. In
modern society, buildings provide very important space for various
activities such as, for example, business, educational,
governmental, organizational, and even activities of daily living
(e.g., an apartment complex). For example, most business activities
happen in the buildings, such as business strategy planning,
business negotiation, customer visit, software development,
hardware design, etc. Providing a comfortable and inviting
environment for occupants of the building is instrumental to
improve their working or living efficiency and productivity, which
however, necessarily requires provision of better illumination, air
conditioning and ventilation. As a result, more energy is
consumed.
Many buildings have meters and sub-meters that measure energy
consumption in various zones of a building and for various
equipment, e.g., chillers, boilers, pumps, air handling unit (AHU),
lighting, plug load, etc. Such meters and sub-meters can provide
high resolution meter data (e.g., by 15-minutes and hourly, etc.)
and a computerized system such as a Building Management System
(BMS) stores such meter data, making them readily available.
However, currently there is no use of online analytics with
adaptive tuning parameters to monitor energy consumption in
buildings. As such, at least two major challenges arise. First,
there is no efficient adaptation to non-stationarities, which can
be symptomatic of abnormal energy consumption. Second, batch
processing (of a power signal) requires having all the data before
starting the processing which delays the detection of abnormal
energy consumption. Accordingly, a need exits for online monitoring
(e.g., an energy management system in a computing environment) of
energy consumption in one or more buildings. The energy management
system may provide 1) fast and efficient decision making upon
abnormal energy consumption detection, 2) fast and efficient
implementation of corrective measures, 3) efficient and less
expensive monitoring with fast detection, and/or 4) a recorder
system to collect, record, and/or send the results of monitoring to
the reading devices only upon abnormal energy consumption
detection.
In one aspect, by way of example only, the present invention
provides for fast detection of abnormal energy consumption of
energy consumption systems of a building using an array of Internet
of Things (IoT) sensors associated with an IoT computing
environment. Energy consumption may be predicted for one or more
facilities according to one or more energy consumption
measurements, weather data, and one or more characteristics of the
one or more facilities, or a combination thereof. An onset of an
energy consumption anomaly may be detected according to the
prediction.
In an additional aspect, the present invention provides for an
energy management system. The energy management system may include
one or more recorder devices (e.g., "smart meters") for recording
energy, buildings specificity, weather for online predictions, and
fast detection of abnormal energy consumption. In one aspect,
buildings specificity may refer to commercial, residential,
corporate, administrative buildings, or other type of buildings
where each building may have an energy consumption pattern (e.g.,
an energy consumption signature unique to the particular building).
Furthermore, in each of the buildings, the energy consumption
pattern may depend on a number of occupants, the number and type of
systems (e.g., heating, ventilation, and air conditioning (HVAC),
computing systems, or other energy consuming systems that may
require the use of energy) present in the buildings, and the like.
Also, the nature of the energy consumption in each building may
differ depending on the time such as, for example, the day, the
week, the month, and/or weekdays to weekend, and from morning to
evening.
A communication system may be provided and connected to the
recording devices associated with the energy management system. The
energy management system may analyze, interpret, and use the
recorded information and display 1) recorded measurements (e.g.,
energy measurements, building data, weather data, etc.), 2)
predicted energy consumption, and/or 3) monitored results of the
consumed energy. A prediction operation for predicting energy
consumption may be performed with any learning operations or
machine learning operations for one or more predictive models
(e.g., kernel recursive least squares, generalized additive model
"GAM") with the specificity of having adaptive tuning parameters
(e.g., use a gradient operation to cause the tuning parameters to
become adaptive). The monitoring is performed with any change-point
detection. It should be noted that the present invention may
operate with both fixed and/or adaptive tuning parameters. To allow
fast detection of anomalies, the present invention may exploit
adaptive tuning parameters.
Also, the tuning parameters may be the parameters that control the
flexibility of an operation or algorithm. The flexibility relating
to the operation or algorithm may be fluctuating (e.g., high
fluctuating) or smooth. The trade-off between high flexibility and
smoothness is fixed by the tuning parameters. As an example, a
tuning parameter may be the size of data (e.g., a window size) upon
which the parameters of a predictive model are computed. Therefore,
the parameters of the predictive model may be changed depending on
the size of the window. In terms of anomaly detection, a large
window (e.g., a larger window of data) may provide reliable, but
delayed detection of anomalies. Conversely, a small window (e.g., a
smaller window of data compared to the larger window of data) may
yield fast detection at the expense of several potential false
alarms due to an increased number of fluctuations. To arrive at a
trade-off, the present invention provides the use of adaptive
tuning parameters for both reliable and fast detection.
In one aspect, the present invention pertains to any closed-loop
thermal energy system (heating and cooling). The benefits and
advantages of the system may include eliminating/reducing the
requirement to shut down a system. The present invention enables
the system to maintain operation, without a system shutdown and
enables sample data from temperature flows of the fluid to be
detected by an IoT sensor secured on one or more positions of the
closed-loop thermal energy system.
In one aspect, the present invention provides for a dual system of
recorders and an energy management system, for detecting the onset
of abnormal energy consumption in buildings. One or more recorder
devices (e.g., IoT sensor devices or "smart meters") may record
energy measurements, weather and building characteristics, and
compute online residuals and detect early onset of abnormal events
when detected. In case of abnormal event detection, the recorder
(smart meters) may notify the energy management system (e.g., a
computer, smartphones, tablets, IoT devices, and the like) about
the presence of energy consumption anomalies. To allow an early
detection of abnormal energy consumption, one or more tuning
parameters may be adaptive for the prediction operation so as to
capture at the earliest stage or occurrence of the presence of
energy anomalies in buildings, and particularly energy consumption
anomalies. In order to increase efficiency and decrease deployment
costs in the context of various buildings, the recorder devices may
integrate prediction and anomaly detection capabilities and
directly output the energy management results, which may be
improved by the adaptive tuning parameters, to one or more
computing devices. In one aspect, the online residuals may be the
residuals that are computed in real-time. The residuals are the
difference between the actual energy consumption and the predicted
energy consumption. When the actual energy consumption and the
predicted energy consumption are equal to zero, there is no
anomaly. When the actual energy consumption and the predicted
energy consumption are different (e.g., none zero), an anomaly
might be present. In the cases where the residuals are different
from zero, the actual energy consumption and the predicted energy
consumption may be transferred to a change-point method, which will
confirm or infirm the presence of an anomaly.
In one aspect, the present invention may detect early onset of
abnormal energy consumption for a building by using predicted
energy consumption. More specifically, the present invention may
predict power consumption for the building based on building
parameters, weather conditions, and other contextual data relating
to energy usage. The predicted power consumption may be compared
with a standardized power consumption threshold of one or more
buildings for detecting onset of abnormal power consumption. A
recording system (e.g., reading systems such as "smart meters") may
be activated for capturing power consumption data so as to identify
an exact location for anomalies and alerting a user. One or more
tuning parameters of prediction models may be dynamically changed
by considering the building parameters, the weather conditions,
energy consumption measurements, or a combination thereof.
In an additional aspect, cognitive or "cognition" may refer to a
mental action or process of acquiring knowledge and understanding
through thought, experience, and one or more senses using machine
learning (which may include using sensor based devices or other
computing systems that include audio or video devices). Cognitive
may also refer to identifying patterns of behavior, leading to a
"learning" of one or more events, operations, or processes. Thus,
the cognitive model may, over time, develop semantic labels to
apply to observed behavior and use a knowledge domain or ontology
to store the learned observed behavior. In one embodiment, the
system provides for progressive levels of complexity in what may be
learned from the one or more events, operations, or processes.
In an additional aspect, the term cognitive may refer to a
cognitive system. The cognitive system may be a specialized
computer system, or set of computer systems, configured with
hardware and/or software logic (in combination with hardware logic
upon which the software executes) to emulate human cognitive
functions. These cognitive systems apply human-like characteristics
to convey and manipulate ideas which, when combined with the
inherent strengths of digital computing, can solve problems with a
high degree of accuracy (e.g., within a defined percentage range or
above an accuracy threshold) and resilience on a large scale. A
cognitive system may perform one or more computer-implemented
cognitive operations that approximate a human thought process while
enabling a user or a computing system to interact in a more natural
manner. A cognitive system may comprise artificial intelligence
logic, such as natural language processing (NLP) based logic, for
example, and machine learning logic, which may be provided as
specialized hardware, software executed on hardware, or any
combination of specialized hardware and software executed on
hardware. The logic of the cognitive system may implement the
cognitive operation(s), examples of which include, but are not
limited to, question answering, identification of related concepts
within different portions of content in a corpus, and intelligent
search algorithms, such as Internet web page searches.
In general, such cognitive systems are able to perform the
following functions: 1) Navigate the complexities of human language
and understanding; 2) Ingest and process vast amounts of structured
and unstructured data; 3) Generate and evaluate hypotheses; 4)
Weigh and evaluate responses that are based only on relevant
evidence; 5) Provide situation-specific advice, insights,
estimations, determinations, evaluations, calculations, and
guidance; 6) Improve knowledge and learn with each iteration and
interaction through machine learning processes; 7) Enable decision
making at the point of impact (contextual guidance); 8) Scale in
proportion to a task, process, or operation; 9) Extend and magnify
human expertise and cognition; 10) Identify resonating, human-like
attributes and traits from natural language; 11) Deduce various
language specific or agnostic attributes from natural language; 12)
Memorize and recall relevant data points (images, text, voice)
(e.g., a high degree of relevant recollection from data points
(images, text, voice) (memorization and recall)); and/or 13)
Predict and sense with situational awareness operations that mimic
human cognition based on experiences.
Additional aspects of the present invention and attendant benefits
will be further described, following.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as Follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, tablets, and the like).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as Follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as Follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
system memory 28 may include at least one program product having a
set (e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in system memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
In the context of the present invention, and as one of skill in the
art will appreciate, various components depicted in FIG. 1 may be
located in a moving vehicle. For example, some of the processing
and data storage capabilities associated with mechanisms of the
illustrated embodiments may take place locally via local processing
components, while the same components are connected via a network
to remotely located, distributed computing data processing and
storage components to accomplish various purposes of the present
invention. Again, as will be appreciated by one of ordinary skill
in the art, the present illustration is intended to convey only a
subset of what may be an entire connected network of distributed
computing components that accomplish various inventive aspects
collectively.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Device layer 55 includes physical and/or virtual devices, embedded
with and/or standalone electronics, sensors, actuators, and other
objects to perform various tasks in a cloud computing environment
50. Each of the devices in the device layer 55 incorporates
networking capability to other functional abstraction layers such
that information obtained from the devices may be provided thereto,
and/or information from the other abstraction layers may be
provided to the devices. In one embodiment, the various devices
inclusive of the device layer 55 may incorporate a network of
entities collectively known as the "internet of things" (IoT). Such
a network of entities allows for intercommunication, collection,
and dissemination of data to accomplish a great variety of
purposes, as one of ordinary skill in the art will appreciate.
Device layer 55 as shown includes sensor 52, actuator 53,
"learning" thermostat 56 with integrated processing, sensor, and
networking electronics, camera 57, controllable household
outlet/receptacle 58, and controllable electrical switch 59 as
shown. Other possible devices may include, but are not limited to
various additional sensor devices, networking devices, electronics
devices (such as a remote control device), additional actuator
devices, so called "smart" appliances such as a refrigerator or
washer/dryer, and a wide variety of other possible interconnected
objects.
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provides cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provides pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and, in the
context of the illustrated embodiments of the present invention,
various workloads and functions 96 for cognitive detection of
energy consumption. In addition, workloads and functions 96 for
cognitive detection of energy consumption may include such
operations as data analysis (including data collection and
processing from various environmental sensors), and predictive data
analytics functions. One of ordinary skill in the art will
appreciate that the workloads and functions 96 for cognitive
detection of energy consumption may also work in conjunction with
other portions of the various abstractions layers, such as those in
hardware and software 60, virtualization 70, management 80, and
other workloads 90 (such as data analytics processing 94, for
example) to accomplish the various purposes of the illustrated
embodiments of the present invention.
Turning now to FIG. 4, a block diagram depicting exemplary
functional components 400 according to various mechanisms of the
illustrated embodiments is shown. FIG. 4 illustrates cognitive
energy consumption anomaly detection and training of a machine
learning model in a computing environment, such as a computing
environment 402, according to an example of the present technology.
As will be seen, many of the functional blocks may also be
considered "modules" or "components" of functionality, in the same
descriptive sense as has been previously described in FIGS. 1-3.
With the foregoing in mind, the module/component blocks 400 may
also be incorporated into various hardware and software components
of a system for accurate temporal event predictive modeling in
accordance with the present invention. Many of the functional
blocks 400 may execute as background processes on various
components, either in distributed computing components, or on the
user device, or elsewhere. Computer system/server 12 is again
shown, incorporating processing unit 16 and memory 28 to perform
various computational, data processing and other functionality in
accordance with various aspects of the present invention.
The system 400 may include the computing environment 402 (e.g.,
included in a heat exchange system/unit), an energy management
system 430, and a device 420, such as a desktop computer, laptop
computer, tablet, smartphone, and/or another electronic device that
may have one or more processors and memory. The device 420, the
energy management system 430, and the computing environment 402 may
each be associated with and/or in communication with each other, by
one or more communication methods, such as a computing network. In
one example, the device 420 and/or the energy management system 430
may be controlled by an owner, customer, or
technician/administrator associated with the computing environment
402. In another example, the device 420 and/or the energy
management system 430 may be completely independent from the owner,
customer, or technician/administrator of the computing environment
402.
In one aspect, the computing environment 402 may provide
virtualized computing services (i.e., virtualized computing,
virtualized storage, virtualized networking, etc.) to devices 420.
More specifically, the computing environment 402 may provide
virtualized computing, virtualized storage, virtualized networking
and other virtualized services that are executing on a hardware
substrate.
As depicted in FIG. 4, the computing environment 402 may include a
machine learning module 406, a features and/or parameters 404
(e.g., "tuning parameters" of a predictive model) that is
associated with a machine learning module 406, and the energy
management system 430. The features and/or parameters database 404
may also include energy usage profiles, building profiles (e.g.,
building characteristics or parameters) for each energy management
system 430 (that may be included in one or more buildings) and/or
IoT sensor devices (e.g., "smart readers") associated with an IoT
sensor component 416. It should be noted that one or more IoT
sensor devices may be represented as the IoT sensor component 416
may be coupled to the energy management system 430. In one aspect,
the IoT sensor component 416 may be a smart meter that may record
consumption of electric energy. The smart meter may record the
consumption of electric energy in selected time intervals and
communicate that information at various selected periods of time.
In an additional aspect, the IoT sensor component 416 may be
associated with one or more smart meters for collecting, recording,
and measuring energy consumption in one or more buildings.
The features and/or parameters 404 may be a combination of
features, tuning parameters, building characteristics, energy
consumption data, temperature data, historical data, tested and
validated data, or other specified/defined data for testing,
monitoring, validating, detecting, learning, analyzing and/or
calculating various conditions or diagnostics relating to
cognitively detecting energy consumption anomalies in the energy
management system 430. That is, different combinations of
parameters may be selected and applied to the input data for
learning or training one or more machine learning models of the
machine learning module 406. The features and/or parameters 404 may
define one or more settings of the IoT sensors (e.g., smart meters)
associated with the IoT sensor component 416 to enable the
collecting, recording, and measuring energy consumption in one or
more buildings. The one or more the IoT sensors (e.g., smart
meters) associated with the IoT sensor component 416 may be coupled
to the energy management system 430 at one or more defined
distances from alternative IoT sensors (e.g., smart meters).
The computing environment 402 may also include a computer system
12, as depicted in FIG. 1. The computer system 12 may also include
the energy management component 410, a building/facility component
412, and an IoT sensor component 416 each associated with the
machine learning module for training and learning one or more
machine learning models and also for applying multiple combinations
of features, tuning parameters, building characteristics, energy
consumption profiles for each building, normalized/standardized
energy consumption values, previously estimated energy consumption
values, temperature data, or a combination thereof to the machine
learning model for cognitive detection of energy consumption
anomalies in one or more buildings.
In one aspect, the building/facility component 412 includes data
relating to the characteristics, parameters, features, and/or
energy consumption information relating to each building or
facility in association with the energy management system 430.
In one aspect, the machine learning module 406 may include an
estimation/prediction component 408 for cognitively predicting
energy consumption for one or more facilities according to one or
more energy consumption measurements, weather data, and one or more
characteristics of the one or more facilities, or a combination
thereof. The machine learning module 406 may collect feedback
information from the one or more IoT sensors/smart meters
associated with the IoT sensor component 416 to cognitively learn
or estimate one or more tuning parameters of one or more prediction
models for predicting the energy consumption, and/or dynamically
change one or more tuning parameters of one or more prediction
models for predicting the energy consumption (in association with
the energy management component 410). The machine learning module
406 may use the feedback information to provide cognitive detection
of energy consumption and detect an onset of an energy consumption
anomaly according to the prediction using the estimation/prediction
component 408. The machine learning module 406 may be initialized
using feedback information to learn behavior of the energy
management system 430 for each particular building.
The energy management component 410 may perform a monitoring
operation. The monitoring operation may perform 1) data cleaning,
2) energy consumption forecasting for residual generation, and 3)
change point detection. In one aspect, the data cleaning process
may detect one or more missing values and outliers and compensate
the values and/or outliers by using a median filter or other
interpolation operation. In one aspect, an outlier may be any value
(datum) that is far distant from the other values (data) of the
energy consumption. For example, if for a given building on Monday
at 9:00 a.m. the usual energy consumption is 900 kilowatts ("KW"),
when for a similar day (Monday) the recorded value at 9:00 a.m. is
90000 KW, then this value is an outlier. Furthermore, if at 9:00 am
for the similar day (Monday) the recorded value is 3 KW, this is
also an outlier, because it deviates from the usual recording. An
exception to this rule may be a defined holiday or vacation days. A
missing value may be an existing value that a recorder missed and
failed to capture. The missing value may be recognized by a NAN
("Not A Number") signature.
Moreover, detecting an outlier may include detecting the values
that fall outside a normal or standardized range of the energy
consumption. Detecting missing values may include detecting the NAN
or any other pre-specified signature and reflecting missing values.
One or more outlier detection operations may be used such as, for
example, by setting a threshold value for each day of the week and
time of the day and detect values overtaking the thresholds, which
may then be considered as outliers. A median filter may be used to
replace the missing values and/or outliers by the median values,
where the values considered are those corresponding to the similar
day, hour, and minutes.
The energy management component 410, in association with the
machine learning module 406 and/or the energy management system
430, may predict energy/power consumption for each building based
on building parameters, weather conditions, and energy/power
consumption of each building so to compare the predicted
energy/power consumption with a standardized amount of energy/power
consumption of one or more buildings for detecting early onset of
abnormal power consumption.
The energy management component 410, in association with the IoT
sensor component 416, may enable one or more reading systems (IoT
sensors or smart meters) for capturing power consumption data in
the one or more buildings so as to identify exact location for
energy/power consumption anomalies.
The energy management component 410 may alert a user (e.g., via
device 420) of the detected energy/power consumption anomalies. The
device 420 may include a graphical user interface (GUI) 422 enabled
to display on the device 420 one or more user interface controls
for a user to interact with the GUI 422. For example, the GUI 422
may display the detected energy/power consumption anomalies to a
user via an interactive graphical user interface (GUI) according to
the cognitive detection of energy consumption anomalies in the
energy management system. For example, the output to the device may
be an alert that indicates or displays audibly and/or visually on
the GUI 422 "ALERT! An energy anomaly is detected in sector "A" of
building A."
In one aspect, the cognitive detection of energy consumption
anomalies in an energy system and estimation/predictive modeling
(or machine learning modeling), as described herein, may be
performed using a wide variety of methods or combinations of
methods, such as supervised learning, unsupervised learning,
temporal difference learning, reinforcement learning and so forth.
Some non-limiting examples of supervised learning which may be used
with the present technology include AODE (averaged one-dependence
estimators), artificial neural network, backpropagation, Bayesian
statistics, naive bays classifier, Bayesian network, Bayesian
knowledge base, case-based reasoning, decision trees, inductive
logic programming, Gaussian process regression, gene expression
programming, group method of data handling (GMDH), learning
automata, learning vector quantization, minimum message length
(decision trees, decision graphs, etc.), lazy learning,
instance-based learning, nearest neighbor algorithm, analogical
modeling, probably approximately correct (PAC) learning, ripple
down rules, a knowledge acquisition methodology, symbolic machine
learning algorithms, sub symbolic machine learning algorithms,
support vector machines, random forests, ensembles of classifiers,
bootstrap aggregating (bagging), boosting (meta-algorithm), ordinal
classification, regression analysis, information fuzzy networks
(IFN), statistical classification, linear classifiers, fisher's
linear discriminant, logistic regression, perceptron, support
vector machines, quadratic classifiers, k-nearest neighbor, hidden
Markov models and boosting. Some non-limiting examples of
unsupervised learning which may be used with the present technology
include artificial neural network, data clustering,
expectation-maximization, self-organizing map, radial basis
function network, vector quantization, generative topographic map,
information bottleneck method, IBSEAD (distributed autonomous
entity systems based interaction), association rule learning,
apriori algorithm, eclat algorithm, FP-growth algorithm,
hierarchical clustering, single-linkage clustering, conceptual
clustering, partitional clustering, k-means algorithm, fuzzy
clustering, and reinforcement learning. Some non-limiting example
of temporal difference learning may include Q-learning and learning
automata. Specific details regarding any of the examples of
supervised, unsupervised, temporal difference or other machine
learning described in this paragraph are known and are within the
scope of this disclosure. Also, when deploying one or more machine
learning models, a computing device may be first tested in a
controlled environment before being deployed in a public setting.
Also even when deployed in a public environment (e.g., external to
the controlled, testing environment), the computing devices may be
monitored for compliance.
In one aspect, the computing system 12/computing environment 402
may perform one or more calculations according to mathematical
operations or functions that may involve one or more mathematical
operations (e.g., solving differential equations or partial
differential equations analytically or computationally, using
addition, subtraction, division, multiplication, standard
deviations, means, averages, percentages, statistical modeling
using statistical distributions, by finding minimums, maximums or
similar thresholds for combined variables, etc.).
FIG. 5 is a flowchart diagram of an exemplary method for cognitive
detection of energy consumption anomalies using one or more
Internet of Things (IoT) devices ("smart recorders") operating
within a computing network, in which various aspects of the
illustrated embodiments may be implemented. The blocks of
functionality 500 may also be incorporated into various hardware
and software components of FIGS. 1-4. The functionality 500 may be
implemented as a method executed as instructions on a machine,
where the instructions are included on at least one computer
readable medium or one non-transitory machine-readable storage
medium.
The functionality 500 may start in block 502. Energy consumption
monitoring may be started/commenced (e.g., for one or more
buildings), as in block 504. A monitoring device may be
initialized, as in block 506. That is, the monitoring component may
be a recorder device (e.g., a "smart" recorder or IoT sensor
device) that may collect and/or record energy consumption
measurements, perform one or more predictions, detect energy
consumption anomalies, and/or energy consumptions. Various types of
data may be collected and acquired, as in block 508. The various
types of data may include energy data, building data (e.g.,
characteristics, parameters, design features, and the like),
weather data, and other contextual data relating to the energy
consumption in the buildings.
A determination operation may be performed to determine whether a
reading of the energy consumption, weather, and/or building
characteristics are correct, as in block 510. That is, the
"reading" of the energy consumption is correct if the reading is
not an outlier or a missing value, as described above. From block
510, if the reading of the energy consumption, weather, and/or
building characteristics are not correct, the functionality 500 may
return to step 506. Alternatively, if the reading of the energy
consumption, weather, and/or building characteristics are correct,
a monitoring component may be activated, as in block 512.
Monitoring results may be transmitted to one or more computing
devices of a user (e.g., via a graphical user interface "GUI") upon
detection of an energy consumption anomaly, as in block 514. The
functionality 500 may end in block 516.
FIG. 6 is a flowchart diagram of an exemplary method for monitoring
energy consumption in one or more facilities operating within a
computing network, in which various aspects of the illustrated
embodiments may be implemented. The blocks of functionality 600 may
also be incorporated into various hardware and software components
of FIGS. 1-4. The functionality 600 may be implemented as a method
executed as instructions on a machine, where the instructions are
included on at least one computer readable medium or one
non-transitory machine-readable storage medium.
The functionality 600 may start in block 602. A data cleaning
process may be performed, as in block 604. Energy consumption (of
one or more facilities/buildings) may be forecasted to generate one
or more residuals, as in block 606. A change point detection
operation may be applied to the residuals, as in block 608. The
functionality 600 may end in block 610.
FIG. 7 is a flowchart diagram of an exemplary method for
forecasting energy consumption in one or more facilities in an IoT
computing network, in which various aspects of the illustrated
embodiments may be implemented. The blocks of functionality 700 may
also be incorporated into various hardware and software components
of FIGS. 1-4. The functionality 700 may be implemented as a method
executed as instructions on a machine, where the instructions are
included on at least one computer readable medium or one
non-transitory machine-readable storage medium.
The functionality 700 may start in block 702. Energy data (e.g.,
energy consumption data), weather data, and building data (e.g.,
building characteristics and parameters) may be collected and/or
received, as in block 704. A predicted output and residuals may be
determined and/or computed (e.g., determining the difference
between the predicted energy consumption and the actual energy
consumption), as in block 706. A determination operation may be
performed to determine whether one or more residuals are deviating
from a nominalized value for energy consumption, as in block 708.
If the one or more residuals are not deviating from a nominalized
value for energy consumption, the functionality 700 may return to
step 706. If the one or more residuals are deviating from a
nominalized value for energy consumption, one or more tuning
parameters (e.g., of a prediction model) may be updated (e.g.,
dynamically changed), as in block 710. The functionality 700 may
end in block 712.
In one aspect, the operations for forecasting may include using one
or more predictive models (e.g., generalized additive model "GAM",
Kernel with adaptive tuning parameters such as, for example,
regularization, and the like) to predict energy consumption. The
regularization prevents from overfitting (high fluctuation). The
kernel with adaptive tuning parameters means that the
regularization may be updated upon arrival of new energy
consumption data by using, for example, a gradient descent method.
During a prediction operation, one or more residuals may be
generated. Any difference between the actual energy consumption
measurements and the estimated measurements may be compared and
determined. That is, as one or more residuals begin to deviate from
normalized nominal values (e.g., standardized energy consumption
values, a zero value, and/or an energy consumption threshold for a
particular or selected facility/building), one or more tuning
parameters may be estimated so as to accelerate the convergence and
provide fast detection of energy consumption anomalies. That is,
when an anomaly occurs, the parameters of the predictive model of
the energy consumption change from a normal state to an abnormal
state. The transition time between the normal state and the
abnormal state, for the change of the predictive model parameters,
is variable and may depend on the tuning parameters. "Accelerating
the convergence" therefore means reducing that transition time, by
making the tuning parameters adaptive, so as to decide quickly
(e.g., "fast detection") about the presence of an anomaly. Also,
"deviating residuals" means that the actual energy consumption is
different from the predicted one by the model, therefore there is a
change in the parameters of the model, considered as a reference.
This indicates a potential presence of an anomaly.
FIG. 8 is a method 800 for detection of energy consumption
anomalies of one or more facilities (e.g., buildings) in an IoT
computing network, in which various aspects of the illustrated
embodiments may be implemented. The blocks of functionality 800 may
also be incorporated into various hardware and software components
of FIGS. 1-4. The functionality 800 may be implemented as a method
executed as instructions on a machine, where the instructions are
included on at least one computer readable medium or one
non-transitory machine-readable storage medium. The functionality
800 may start in block 802. Energy consumption may be predicted for
one or more facilities according to one or more energy consumption
measurements, weather data, and one or more characteristics of the
one or more facilities, or a combination thereof, as in block 804.
An onset of an energy consumption anomaly may be detected according
to the prediction, as in block 806. The functionality 800 may end
in block 808.
In one aspect, in conjunction with and/or as part of at least one
block of FIG. 8, the operations of method 800 may include each of
the following. The operations of method 800 may predict energy
consumption (e.g., power consumption) for one or more buildings
based on building parameters, weather conditions, and/or energy
consumption measurements and compare the predicted power
consumption with a standard power consumption (e.g., a standardized
energy consumption amount) for detecting an early/premature onset
of abnormal power consumption. The operations of method 800 may
activate reading systems (e.g., one or more IoT sensor devices or
"smart meters") for capturing power consumption data to identify
exact locations for one or more anomalies and then alerting a user.
The operations of method 800 may dynamically change tuning
parameters of prediction models by considering the building
parameters, the weather conditions, energy consumption
measurements, or other user defined parameters for predicting the
energy consumption levels.
In an additional aspect, the operations of method 800 may alert one
or more Internet of Things (IoT) devices of the energy consumption
anomaly. The operations of method 800 may identify a location of
the energy consumption anomaly according to energy consumption data
collected by one or more IoT sensor devices associated with the one
or more facilities, monitor the energy consumption using a change
point detection operation, a hypothesis test, or a combination
thereof. The one or more IoT sensor devices may be in an IoT
computing network.
In additional aspects, the operations of method 800 may cognitively
learn or estimate one or more tuning parameters of one or more
prediction models for predicting the energy consumption, and/or
dynamically change one or more tuning parameters of one or more
prediction models for predicting the energy consumption. The
operations of method 800 may compare the predicted energy
consumption with an energy consumption threshold to detect the
energy consumption anomaly. A machine learning mechanism may be
implemented using the collected feedback information to assist with
the learning or estimating one or more tuning parameters of one or
more prediction models for predicting the energy consumption. The
machine learning mechanism may also be used to predict the energy
consumption for one or more buildings (e.g., facilities).
The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a
computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowcharts and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowcharts and/or
block diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowcharts and/or block diagram block or blocks.
The flowcharts and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowcharts or block diagrams may
represent a module, segment, or portion of instructions, which
comprises one or more executable instructions for implementing the
specified logical function(s). In some alternative implementations,
the functions noted in the block may occur out of the order noted
in the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustrations, and combinations
of blocks in the block diagrams and/or flowchart illustrations, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *